As video becomes increasingly central to information dissemination and multimodal large language models (MLLMs) continue to advance, evaluating video retrieval has become increasingly important. In realistic search scenarios, this requires matching short user queries to long-form content using both visual and auditory evidence. Yet existing retrieval benchmarks are still dominated by short clips, single modalities, and caption-based evaluation. We introduce FLARE, a full-modality long-video audiovisual retrieval benchmark with user-simulated queries. Built from 399 carefully screened Video-MME videos (10--60 min, 225.4 h) to ensure source quality and diversity, FLARE contains 87,697 clips annotated with vision, audio, and unified audiovisual captions, together with 274,933 user-style queries. Cross-modal queries are further filtered by a hard bimodal constraint, requiring retrieval to fail under either modality alone but succeed when both are combined. FLARE evaluates models under two regimes, caption-based and query-based retrieval, across vision, audio, and unified audiovisual settings. Experiments with 15 representative retrievers show that user-style queries substantially change model behavior, strong caption-based performance does not always transfer to query-based retrieval, and audio--language alignment remains a key bottleneck for unified audiovisual retrieval. Our code and data are released at https://flarebench.github.io/

翻译：随着视频成为信息传播的核心载体，多模态大语言模型持续进步，视频检索评估的重要性日益凸显。在实际搜索场景中，这需要利用视觉和听觉证据将简短用户查询与长视频内容相匹配。然而现有检索基准仍以短视频片段、单模态和基于描述的评估为主。我们提出FLARE——一个面向用户模拟查询的全模态长视频视听检索基准。基于399个经严格筛选的Video-MME视频（10-60分钟，总计225.4小时）以确保源质量与多样性，FLARE包含标注有视觉、音频及统一视听描述的87,697个片段，以及274,933条用户风格查询。跨模态查询进一步通过硬双模态约束过滤，要求仅依赖单模态时检索失败，而双模态结合时成功。FLARE在视觉、音频及统一视听设置下，分别在基于描述和基于查询的两种检索模式中评估模型。15个代表性检索器的实验表明：用户风格查询显著改变模型行为；强基于描述性能并不总能迁移至基于查询检索；音频-语言对齐仍是统一视听检索的关键瓶颈。我们的代码与数据已发布在https://flarebench.github.io/